通过双流网络学习视觉抽象推理
Learning Visual Abstract Reasoning through Dual-Stream Networks
AI总结：
针对深度神经网络在视觉抽象推理任务中的局限，提出受视觉处理双通路假说启发的双流推理网络（DRNet），通过双流捕获特征并经推理模块提取规则，在多个RPM基准上实现最优平均性能且泛化能力强。
摘要：
视觉抽象推理任务对深度神经网络构成挑战，暴露了其能力的局限性。在这项工作中，我们提出了一种神经网络模型，以应对瑞文渐进矩阵（RPM）带来的挑战。受视觉处理双通路假说的启发，我们引入了双流推理网络（DRNet），该网络利用两个并行分支捕获图像特征。在两个流的基础上，推理模块首先学习合并同一图像的高级特征，然后使用规则提取器处理包含八张上下文图像和每张候选图像的组合，提取离散的抽象规则，并利用多层感知机（MLP）进行预测。实证结果表明，所提出的DRNet在多个RPM基准上实现了最先进的平均性能。此外，DRNet展示了强大的泛化能力，甚至扩展到各种分布外场景。DRNet中的双流通过处理局部或空间信息发挥不同的功能，然后被整合到推理模块中，利用抽象规则促进视觉推理任务的执行。这些发现表明，双流架构在视觉抽象推理中可以发挥关键作用。
英文摘要：
Visual abstract reasoning tasks present challenges for deep neural networks, exposing limitations in their capabilities. In this work, we present a neural network model that addresses the challenges posed by Raven's Progressive Matrices (RPM). Inspired by the two-stream hypothesis of visual processing, we introduce the Dual-stream Reasoning Network (DRNet), which utilizes two parallel branches to capture image features. On top of the two streams, a reasoning module first learns to merge the high-level features of the same image. Then, it employs a rule extractor to handle combinations involving the eight context images and each candidate image, extracting discrete abstract rules and utilizing an multilayer perceptron (MLP) to make predictions. Empirical results demonstrate that the proposed DRNet achieves state-of-the-art average performance across multiple RPM benchmarks. Furthermore, DRNet demonstrates robust generalization capabilities, even extending to various out-of-distribution scenarios. The dual streams within DRNet serve distinct functions by addressing local or spatial information. They are then integrated into the reasoning module, leveraging abstract rules to facilitate the execution of visual reasoning tasks. These findings indicate that the dual-stream architecture could play a crucial role in visual abstract reasoning.
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